Sensor signals are gaining high importance for deriving parameters required to build smart applications based on sensor analytics. Hence extracting the various time series features of sensor signals and then co-relating them with application specific parameters is a necessity to obtain robust sensor analytics applications. However, sensor signals such as Photoplethysmograph (PPG) are characterized by a lot of noise and analytics generally run on low power/battery operated device like mobile phones. Therefore, identifying outlier/anomaly (with or without physiological abnormality) with reduced error and reduced resource usage is an important requirement.